Vision is inherently dynamic, whether you are Roger Federer or live a sedentary life, as our eyes move around constantly, requiring brain mechanisms to maintain perceptual stability. Eye movement (saccadic) adaptation, driven by visual error signals, has an important role in perceptual stability, ensuring that gaze lands on the intended target. We have learned a great deal about saccadic adaptation by simple behavioural tasks, most often asking to look at a dot on an otherwise empty screen. However, during natural viewing movement statistics and visual signals are very different. This can impact attempts to visual rehabilitation, for instance, where gains are meant to generalize outside the lab.

In a first phase, we will establish a new gaze-contingent paradigm to generate saccadic adaptation during natural viewing. We will induce adaptation by shifting a natural image during saccades and measure the adaptation state in error clamp trials. We will then evaluate the generalizability of visually-induced motor learning compared to classical paradigms [e.g. 1].

In a second phase, we will co-register EEG and eye movements. Fixation event-related potentials (fERPs)—i.e. EEG waveforms locked to fixation onset—will be recorded during natural viewing to detect cortical correlates of visual prediction errors. The sub-cortical physiology driving saccadic adaptation is well researched, but little is known about the cortical processing of the visual error signals that contribute to motor adaptation. We aim to identify cortical processing of prediction errors during natural viewing by using recent pattern-classification techniques on fERPs.

We will build on Leicester’s Centre for Systems Neuroscience expertise in decoding EEG signals [2] and recording fERPs during natural viewing [3].

The supervisory team has excellent track record in the field and will support the applicant with the techniques employed: psychophysics, EEG analysis, eye tracking, and programming with MATLAB.